Optimising filtering parameters for a 3D motion analysis system

Schreven, Sander; Beek, Peter J.; Smeets, Jeroen B. J.

doi:10.1016/j.jelekin.2015.06.004

Cited by 47 publications

(37 citation statements)

References 16 publications

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“…After the cross-correlation, the MVN data were low-pass filtered with a zero-phase 10th order Butterworth filter using a cut-off frequency of 8 Hz. According to Schreven et al [33], the optimal cut-off frequency for filtering human motion data is about 8 Hz. By means of the two filters (i.e., the one during exporting and the one in Matlab), we ensured that the signal was not contaminated with high-frequency sensor noise, while still capturing rapid limb motions of the participant.…”

Section: Data Reductionmentioning

confidence: 99%

How Do eHMIs Affect Pedestrians’ Crossing Behavior? A Study Using a Head-Mounted Display Combined with a Motion Suit

2019

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In future trac, automated vehicles may be equipped with external human-machine interfaces (eHMIs) that can communicate with pedestrians. Previous research suggests that, during first encounters, pedestrians regard text-based eHMIs as clearer than light-based eHMIs. However, in much of the previous research, pedestrians were asked to imagine crossing the road, and unable or not allowed to do so. We investigated the effects of eHMIs on participants’ crossing behavior. Twenty-four participants were immersed in a virtual urban environment using a head-mounted display coupled to a motion-tracking suit. We manipulated the approaching vehicles’ behavior (yielding, nonyielding) and eHMI type (None, Text, Front Brake Lights). Participants could cross the road whenever they felt safe enough to do so. The results showed that forward walking velocities, as recorded at the pelvis, were, on average, higher when an eHMI was present compared to no eHMI if the vehicle yielded. In nonyielding conditions, participants showed a slight forward motion and refrained from crossing. An analysis of participants’ thorax angle indicated rotation towards the approaching vehicles and subsequent rotation towards the crossing path. It is concluded that results obtained via a setup in which participants can cross the road are similar to results from survey studies, with eHMIs yielding a higher crossing intention compared to no eHMI. The motion suit allows investigating pedestrian behaviors related to bodily attention and hesitation.

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Section: Data Reductionmentioning

confidence: 99%

How Do eHMIs Affect Pedestrians’ Crossing Behavior? A Study Using a Head-Mounted Display Combined with a Motion Suit

2019

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“…While no clear trend could be derived for the best ranked combinations of GRF data, most of the best ranked combinations of jerk data were filtered. To our knowledge, this analysis was the first that investigated whether a filter (using an optimal filter cut-off frequency) affects the prediction accuracy of GRF data in human gait (Schreven et al, 2015). The present findings suggest that machine-learning classification should use filtered GRF data.…”

Section: Grf Filteringmentioning

confidence: 62%

Systematic Comparison of the Influence of Different Data Preprocessing Methods on the Performance of Gait Classifications Using Machine Learning

Burdack

Horst

Giesselbach

et al. 2020

Front. Bioeng. Biotechnol.

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Human movements are characterized by highly non-linear and multi-dimensional interactions within the motor system. Therefore, the future of human movement analysis requires procedures that enhance the classification of movement patterns into relevant groups and support practitioners in their decisions. In this regard, the use of data-driven techniques seems to be particularly suitable to generate classification models. Recently, an increasing emphasis on machine-learning applications has led to a significant contribution e.g. in increasing the classification accuracy. In order to ensure the generalizability of the machine-learning models, different data preprocessing steps are usually carried out to process the measured raw data before the classifications. In the past, various methods have been used for each of these preprocessing steps. However, there are hardly any standard procedures or rather systematic comparisons of these different methods and their impact on the classification accuracy. Therefore, the aim of this analysis is to compare different combinations of commonly applied data preprocessing steps and test their effects on the classification accuracy of gait patterns.A publicly available dataset on intra-individual changes of gait patterns was used for this analysis. Forty-two healthy subjects performed 6 sessions of 15 gait trials for one day. For each trial, two force plates recorded the three-dimensional ground reaction forces (GRF). The data was preprocessed with the following steps: GRF filtering, time derivative, time normalization, data reduction, weight normalization and data scaling. Subsequently, combinations of all methods from each individual preprocessing step were analyzed and compared with respect to their prediction accuracy in a six-session classification using Support Vector Machines, Random Forest Classifiers and Multi-Layer Perceptrons. 1 arXiv:1911.04335v1 [cs.LG] 11 Nov 2019 Burdack et al. Data Preprocessing in Gait ClassificationThe results indicate that filtering GRF data and a supervised data reduction (e.g., using Principal Components Analysis) lead to increased prediction accuracies of the machine-learning classifiers. Interestingly, the weight normalization and the number of data points (above a certain minimum) in the time normalization does not have a substantial effect. In conclusion, the present results provide first domain-specific recommendations for commonly applied data preprocessing methods and might help to build more comparable and more robust classification models based on machine learning that are suitable for a practical application.

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“…It is possible to expand this technique towards multiple LED tracking for a full 3D analysis. This would simplify video-based 3D motion and coordination analysis [34] to understand the relationship between body actions and acceleration. In combination with augmented reality tools automatic LED tracking may become a powerful interactive method to optimize interventions in stroke training.…”

Section: Discussionmentioning

confidence: 99%

Automated LED tracking to measure instantaneous velocities in swimming

et al. 2018

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In this paper a video-based method to automatically track instantaneous velocities of a swimmer is presented. Single cameras were used to follow a marker (LED) attached to the body. The method is inspired by particle tracking techniques, traditionally used in the field of fluid dynamics, to measure local velocities of a fluid flow. During the validation experiment, a white LED was attached to the hip of a swimmer together with a speedometer. A swimmer performed four different stroke types. The velocity profiles using LED tracking were captured and showed less noise than the speedometer measurements. Only at times when the marker disappeared above the water surface due to body role in front crawl and backstroke swimming did the LED tracking fail to capture the athlete's motion. The algorithm was tested in a 2D case with a single LED to illustrate the proof of principle, but should be suitable for implementation in a 3D analysis or multiple LED analysis.

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Optimising filtering parameters for a 3D motion analysis system

Cited by 47 publications

References 16 publications

How Do eHMIs Affect Pedestrians’ Crossing Behavior? A Study Using a Head-Mounted Display Combined with a Motion Suit

How Do eHMIs Affect Pedestrians’ Crossing Behavior? A Study Using a Head-Mounted Display Combined with a Motion Suit

Systematic Comparison of the Influence of Different Data Preprocessing Methods on the Performance of Gait Classifications Using Machine Learning

Automated LED tracking to measure instantaneous velocities in swimming

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